Input encoding for classifier generalization

Abstract

Techniques for classifier generalization in a supervised learning process using input encoding are provided. In one aspect, a method for classification generalization includes: encoding original input features from at least one input sample {right arrow over (x)}with a uniquely decodable code using an encoder E(⋅) to produce encoded input features E({right arrow over (x)}), wherein the at least one input sample {right arrow over (x)}comprises uncoded input features; feeding the uncoded input features and the encoded input features E({right arrow over (x)}) to a base model to build an encoded model; and learning a classification function {tilde over (C)}(⋅) using the encoded model, wherein the classification function {tilde over (C)}(⋅) learned using the encoded model is more general than that learned using the uncoded input features alone.